01772nas a2200157 4500008004100000245005500041210005500096260000800151300001400159490000700173520128200180100002301462700002301485700002101508856008501529 2005 eng d00aStochastic model for first order reaction networks0 aStochastic model for first order reaction networks cSEP a901–9460 v673 a

A stochastic model for a general system of first-order reactions in which each reaction may be either a conversion reaction or a catalytic reaction is derived. The governing master equation is formulated in a manner that explicitly separates the effects of network topology from other aspects, and the evolution equations for the first two moments are derived. We find the surprising, and apparently unknown, result that the time evolution of the second moments can be represented explicitly in terms of the eigenvalues and projections of the matrix that governs the evolution of the means. The model is used to analyze the effects of network topology and the reaction type on the moments of the probability distribution. In particular, it is shown that for an open system of first-order conversion reactions, the distribution of all the system components is a Poisson distribution at steady state. Two different measures of the noise have been used previously, and it is shown that different qualitative and quantitative conclusions can result, depending on which measure is used. The effect of catalytic reactions on the variance of the system components is also analyzed, and the master equation for a coupled system of first-order reactions and diffusion is derived.

1 aGadgil, Chetan, J.1 aLee, Chang, Hyeong1 aOthmer, Hans, G. uhttp://library.ncl.res.in/content/stochastic-model-first-order-reaction-networks01076nas a2200133 4500008004100000245004800041210004800089260000800137300001000145490000700155520067800162100002300840856007900863 2008 eng d00aStochastic modeling of biological reactions0 aStochastic modeling of biological reactions cJAN a45-550 v883 a

Several biological systems comprise of reaction networks where the low number of molecules makes it inappropriate to completely characterize system behavior using a continuous approach. Biological systems are also characterized by discrete states (e.g. infected, dead) that are not amenable to the use of a continuous descriptor. This review discusses the need for adopting a discrete stochastic modeling approach for analyzing biological reactions networks. Various stochastic simulation procedures and theoretical studies are presented. The challenges in the theoretical and computational analysis of discrete stochastic biological reaction networks are discussed.

1 aGadgil, Chetan, J. uhttp://library.ncl.res.in/content/stochastic-modeling-biological-reactions02373nas a2200205 4500008004100000022001400041245013600055210006900191260005700260300001400317490000700331520153100338653003401869653002301903653003601926653002101962653003301983100002302016856012802039 2009 eng d a0092-824000aSize-independent differences between the mean of discrete stochastic systems and the corresponding continuous deterministic systems0 aSizeindependent differences between the mean of discrete stochas a233 SPRING ST, NEW YORK, NY 10013 USAbSPRINGERcOCT a1599-16110 v713 a

In this paper, it is shown that for a class of reaction networks, the discrete stochastic nature of the reacting species and reactions results in qualitative and quantitative differences between the mean of exact stochastic simulations and the prediction of the corresponding deterministic system. The differences are independent of the number of molecules of each species in the system under consideration. These reaction networks are open systems of chemical reactions with no zero-order reaction rates. They are characterized by at least two stationary points, one of which is a nonzero stable point, and one unstable trivial solution (stability based on a linear stability analysis of the deterministic system). Starting from a nonzero initial condition, the deterministic system never reaches the zero stationary point due to its unstable nature. In contrast, the result presented here proves that this zero-state is a stable stationary state for the discrete stochastic system, and other finite states have zero probability of existence at large times. This result generalizes previous theoretical studies and simulations of specific systems and provides a theoretical basis for analyzing a class of systems that exhibit such inconsistent behavior. This result has implications in the simulation of infection, apoptosis, and population kinetics, as it can be shown that for certain models the stochastic simulations will always yield different predictions for the mean behavior than the deterministic simulations.

10aBiochemical reaction kinetics10aContinuous systems10aDeterministic chemical kinetics10aDiscrete systems10aStochastic reaction kinetics1 aGadgil, Chetan, J. uhttp://library.ncl.res.in/content/size-independent-differences-between-mean-discrete-stochastic-systems-and-corresponding-001511nas a2200157 4500008004100000245005100041210005100092260000800143300001400151490000600165520103500171100002501206700002101231700002301252856007801275 2011 eng d00aSystems view of the protein expression process0 aSystems view of the protein expression process cDEC a139–1500 v53 a

Many biological processes are regulated by changing the concentration and activity of proteins. The presence of a protein at a given subcellular location at a given time with a certain conformation is the result of an apparently sequential process. The rate of protein formation is influenced by chromatin state, and the rates of transcription, translation, and degradation. There is an exquisite control system where each stage of the process is controlled both by seemingly unregulated proteins as well as through feedbacks mediated by RNA and protein products. Here we review the biological facts and mathematical models for each stage of the protein production process. We conclude that advances in experimental techniques leading to a detailed description of the process have not been matched by mathematical models that represent the details of the process and facilitate analysis. Such an exercise is the first step towards development of a framework for a systems biology analysis of the protein production process.

1 aGokhale, Sucheta, A.1 aNyayanit, Dimpal1 aGadgil, Chetan, J. uhttp://library.ncl.res.in/content/systems-view-protein-expression-process01830nas a2200181 4500008004100000022001400041245009000055210006900145260011000214300001400324490000600338520108900344100002501433700002101458700002701479700002301506856011901529 2012 eng d a1742-206X00aSimple method for incorporating dynamic effects of intronic miRNA mediated regulation0 aSimple method for incorporating dynamic effects of intronic miRN aTHOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLANDbROYAL SOC CHEMISTRYcAPR a2145-21520 v83 a

The importance of microRNA (miRNA) in modulating gene expression at the post-transcriptional level is well known. Such regulation has been shown to influence the dynamics of several regulatory networks including the cell cycle. In this study we incorporated regulatory effects of intronic miRNA into an existing mathematical model of the cell cycle through the use of an existing `proxy' protein - the host protein. It was observed that the incorporation of intronic miRNA mediated regulation improved the performance of the model resulting in a closer match to experimental results. To test the universality of this approach we compared the effects of intronic miRNA mediated regulation and host protein mediated regulation. Further, we compared miRNA mediated and protein mediated positive and negative feedback regulations of the target protein. We found that the target protein profiles were predominantly similar. These observations show the applicability of our method for incorporating intronic miRNA mediated dynamic effects in models for regulation of gene expression.

1 aGokhale, Sucheta, A.1 aHariharan, Manoj1 aBrahmachari, Samir, K.1 aGadgil, Chetan, J. uhttp://library.ncl.res.in/content/simple-method-incorporating-dynamic-effects-intronic-mirna-mediated-regulation-001944nas a2200169 4500008004100000245010400041210006900145260000800214300001400222490000900236520131200245100002601557700001801583700002201601700002301623856012801646 2017 eng d00aSystematic comparison of the response properties of protein and RNA mediated gene regulatory motifs0 aSystematic comparison of the response properties of protein and  cJUN a1235-12450 v13 3 aWe present a framework enabling the dissection of the effects of motif structure (feedback or feedforward), the nature of the controller (RNA or protein), and the regulation mode (transcriptional, post-transcriptional or translational) on the response to a step change in the input. We have used a common model framework for gene expression where both motif structures have an activating input and repressing regulator, with the same set of parameters, to enable a comparison of the responses. We studied the global sensitivity of the system properties, such as steady-state gain, overshoot, peak time, and peak duration, to parameters. We find that, in all motifs, overshoot correlated negatively whereas peak duration varied concavely with peak time. Differences in the other system properties were found to be mainly dependent on the nature of the controller rather than the motif structure. Protein mediated motifs showed a higher degree of adaptation i.e. a tendency to return to baseline levels; in particular, feedforward motifs exhibited perfect adaptation. RNA mediated motifs had a mild regulatory effect; they also exhibited a lower peaking tendency and mean overshoot. Protein mediated feedforward motifs showed higher overshoot and lower peak time compared to the corresponding feedback motifs.1 aIyengar, Bharat, Ravi1 aPillai, Beena1 aVenkatesh, K., V.1 aGadgil, Chetan, J. uhttp://library.ncl.res.in/content/systematic-comparison-response-properties-protein-and-rna-mediated-gene-regulatory-motifs02216nas a2200229 4500008004100000022001400041245006300055210006100118260000900179490000800188520152000196653001501716653002301731653002001754653000901774653001801783100002101801700002001822700002301842700002801865856009301893 2022 eng d a0016-673100aSUMOylation of dorsal attenuates Toll/NF-kappa B signaling0 aSUMOylation of dorsal attenuates TollNFkappa B signaling cJUL 0 v2213 a

In Drosophila, Toll/NF-kappa B signaling plays key roles in both animal development and in host defense. The activation, intensity, and kinetics of Toll signaling are regulated by posttranslational modifications such as phosphorylation, SUMOylation, or ubiquitination that target multiple proteins in the Toll/NF-kappa B cascade. Here, we have generated a CRISPR-Cas9 edited Dorsal (DL) variant that is SUMO conjugation resistant. Intriguingly, embryos laid by dl(SCR) mothers overcome dl haploinsufficiency and complete the developmental program. This ability appears to be a result of higher transcriptional activation by DLSCR. In contrast, SUMOylation dampens DL transcriptional activation, ultimately conferring robustness to the dorso-ventral program. In the larval immune response, dl(SCR) animals show an increase in crystal cell numbers, stronger activation of humoral defense genes, and high cactus levels. A mathematical model that evaluates the contribution of the small fraction of SUMOylated DL (1-5%) suggests that it acts to block transcriptional activation, which is driven primarily by DL that is not SUMO conjugated. Our findings define SUMO conjugation as an important regulator of the Toll signaling cascade, in both development and host defense. Our results broadly suggest that SUMO attenuates DL at the level of transcriptional activation. Furthermore, we hypothesize that SUMO conjugation of DL may be part of a Ubc9-dependent mechanism that restrains Toll/NF-kappa B signaling.

10aDrosophila10ahaploinsufficiency10ainnate immunity10aSUMO10atranscription1 aHegde, Sushmitha1 aSreejan, Ashley1 aGadgil, Chetan, J.1 aRatnaparkhi, Girish, S. uhttp://library.ncl.res.in/content/sumoylation-dorsal-attenuates-tollnf-kappa-b-signaling